---
title: "Example: Context Relevancy | Evals | Kastrax Docs"
description: Example of using the Context Relevancy metric to evaluate how relevant context information is to a query.
---

import { GithubLink } from "@/components/github-link";

# Context Relevancy ✅

This example demonstrates how to use Kastrax's Context Relevancy metric to evaluate how relevant context information is to a given query.

## Overview ✅

The example shows how to:

1. Configure the Context Relevancy metric
2. Evaluate context relevancy
3. Analyze relevancy scores
4. Handle different relevancy levels

## Setup ✅

### Environment Setup

Make sure to set up your environment variables:

```bash filename=".env"
OPENAI_API_KEY=your_api_key_here
```

### Dependencies

Import the necessary dependencies:

```typescript copy showLineNumbers filename="src/index.ts"
import { openai } from '@ai-sdk/openai';
import { ContextRelevancyMetric } from '@kastrax/evals/llm';
```

## Example Usage ✅

### High Relevancy Example

Evaluate a response where all context is relevant:

```typescript copy showLineNumbers{5} filename="src/index.ts"
const context1 = [
  'Einstein won the Nobel Prize for his discovery of the photoelectric effect.',
  'He published his theory of relativity in 1905.',
  'His work revolutionized modern physics.',
];

const metric1 = new ContextRelevancyMetric(openai('gpt-4o-mini'), {
  context: context1,
});

const query1 = 'What were some of Einstein\'s achievements?';
const response1 = 'Einstein won the Nobel Prize for discovering the photoelectric effect and published his groundbreaking theory of relativity.';

console.log('Example 1 - High Relevancy:');
console.log('Context:', context1);
console.log('Query:', query1);
console.log('Response:', response1);

const result1 = await metric1.measure(query1, response1);
console.log('Metric Result:', {
  score: result1.score,
  reason: result1.info.reason,
});
// Example Output:
// Metric Result: { score: 1, reason: 'The context uses all relevant information and does not include any irrelevant information.' }
```

### Mixed Relevancy Example

Evaluate a response where some context is irrelevant:

```typescript copy showLineNumbers{31} filename="src/index.ts"
const context2 = [
  'Solar eclipses occur when the Moon blocks the Sun.',
  'The Moon moves between the Earth and Sun during eclipses.',
  'The Moon is visible at night.',
  'The Moon has no atmosphere.',
];

const metric2 = new ContextRelevancyMetric(openai('gpt-4o-mini'), {
  context: context2,
});

const query2 = 'What causes solar eclipses?';
const response2 = 'Solar eclipses happen when the Moon moves between Earth and the Sun, blocking sunlight.';

console.log('Example 2 - Mixed Relevancy:');
console.log('Context:', context2);
console.log('Query:', query2);
console.log('Response:', response2);

const result2 = await metric2.measure(query2, response2);
console.log('Metric Result:', {
  score: result2.score,
  reason: result2.info.reason,
});
// Example Output:
// Metric Result: { score: 0.5, reason: 'The context uses some relevant information and includes some irrelevant information.' }
```

### Low Relevancy Example

Evaluate a response where most context is irrelevant:

```typescript copy showLineNumbers{57} filename="src/index.ts"
const context3 = [
  'The Great Barrier Reef is in Australia.',
  'Coral reefs need warm water to survive.',
  'Marine life depends on coral reefs.',
  'The capital of Australia is Canberra.',
];

const metric3 = new ContextRelevancyMetric(openai('gpt-4o-mini'), {
  context: context3,
});

const query3 = 'What is the capital of Australia?';
const response3 = 'The capital of Australia is Canberra.';

console.log('Example 3 - Low Relevancy:');
console.log('Context:', context3);
console.log('Query:', query3);
console.log('Response:', response3);

const result3 = await metric3.measure(query3, response3);
console.log('Metric Result:', {
  score: result3.score,
  reason: result3.info.reason,
});
// Example Output:
// Metric Result: { score: 0.12, reason: 'The context only has one relevant piece, while most of the context is irrelevant.' }
```

## Understanding the Results ✅

The metric provides:

1. A relevancy score between 0 and 1:
   - 1.0: Perfect relevancy - all context directly relevant to query
   - 0.7-0.9: High relevancy - most context relevant to query
   - 0.4-0.6: Mixed relevancy - some context relevant to query
   - 0.1-0.3: Low relevancy - little context relevant to query
   - 0.0: No relevancy - no context relevant to query

2. Detailed reason for the score, including analysis of:
   - Relevance to input query
   - Statement extraction from context
   - Usefulness for response
   - Overall context quality

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<hr className="dark:border-[#404040] border-gray-300" />
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<GithubLink
  link={
    "https://github.com/kastrax-ai/kastrax/blob/main/examples/basics/evals/context-relevancy"
  }
/>
